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Abstract Background Appropriate regulation of respective gene expressions is a bottleneck for the realization of artificial biological systems inside living cells. The modification of several promoter sequences is required to achieve appropriate regulation of the systems. However, a time-consuming process is required for the insertion of an operator, a binding site of a protein for gene expression, to the gene regulatory region of a plasmid. Thus, a standardized method for integrating operator sequences to the regulatory region of a plasmid is required. Results We developed a standardized method for integrating operator sequences to the regulatory region of a plasmid and constructed a synthetic promoter that functions as a genetic AND gate. By standardizing the regulatory region of a plasmid and the operator parts, we established a platform for modular assembly of the operator parts. Moreover, by assembling two different operator parts on the regulatory region, we constructed a regulatory device with an AND gate function. Conclusions We implemented a new standard to assemble operator parts for construction of functional genetic logic gates. The logic gates at the molecular scale have important implications for reprogramming cellular behavior.

Abstract Background Elucidating the dynamic behaviour of genetic regulatory networks is one of the most significant challenges in systems biology. However, conventional quantitative predictions have been limited to small networks because publicly available transcriptome data has not been extensively applied to dynamic simulation. Results We present a microarray data-based semi-kinetic (MASK) method which facilitates the prediction of regulatory dynamics of genetic networks composed of recurrently appearing network motifs with reasonable accuracy. The MASK method allows the determination of model parameters representing the contribution of regulators to transcription rate from time-series microarray data. Using a virtual regulatory network and a Saccharomyces cerevisiae ribosomal protein gene module, we confirmed that a MASK model can predict expression profiles for various conditions as accurately as a conventional kinetic model. Conclusion We have demonstrated the MASK method for the construction of dynamic simulation models of genetic networks from time-series microarray data, initial mRNA copy number and first-order degradation constants of mRNA. The quantitative accuracy of the MASK models has been confirmed, and the results indicated that this method enables the prediction of quantitative dynamics in genetic networks composed of commonly used network motifs, which cover considerable fraction of the whole network.

Lymphocyte activation gene (Lag)-3 is an inhibitory co-receptor and target of immune checkpoint inhibitor (ICI) therapy for cancer. The dynamic behavior of Lag-3 was analyzed at the immune synapse upon T-cell activation to elucidate the Lag-3 inhibitory mechanism. Lag-3 formed clusters and co-localized with T-cell receptor microcluster (TCR-MC) upon T-cell activation similar to PD-1. Lag-3 blocking antibodies (Abs) inhibited the co-localization between Lag-3 and TCR-MC without inhibiting Lag-3 cluster formation. Lag-3 also inhibited MHC-II-independent stimulation and Lag-3 Ab, which did not block MHC-II binding could still block Lag-3's inhibitory function, suggesting that the Lag-3 Ab blocks the Lag-3 inhibitory signal by dissociating the co-assembly of TCR-MC and Lag-3 clusters. Consistent with the combination benefit of PD-1 and Lag-3 Abs to augment T-cell responses, bispecific Lag-3/PD-1 antagonists effectively inhibited both cluster formation and co-localization of PD-1 and Lag-3 with TCR-MC. Therefore, Lag-3 inhibits T-cell activation at TCR-MC, and the target of Lag-3 ICI is to dissociate the co-localization of Lag-3 with TCR-MC., Competing Interests: EB and RWM were employed by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA, during the course of this research work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hashimoto-Tane, Bowman, Sakuma, Yoneda, Yugi, de Waal Malefyt and Saito.)

Dysregulation of liver metabolism associated with obesity during feeding and fasting leads to the breakdown of metabolic homeostasis. However, the underlying mechanism remains unknown. Here, we measured multi-omics data in the liver of wild-type and leptin-deficient obese ( ob / ob ) mice at ad libitum feeding and constructed a differential regulatory trans-omic network of metabolic reactions. We compared the trans-omic network at feeding with that at 16 h fasting constructed in our previous study. Intermediate metabolites in glycolytic and nucleotide metabolism decreased in ob / ob mice at feeding but increased at fasting. Allosteric regulation reversely shifted between feeding and fasting, generally showing activation at feeding while inhibition at fasting in ob / ob mice. Transcriptional regulation was similar between feeding and fasting, generally showing inhibiting transcription factor regulations and activating enzyme protein regulations in ob / ob mice. The opposite metabolic dysregulation between feeding and fasting characterizes breakdown of metabolic homeostasis associated with obesity., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)

Biochemical network visualization is one of the essential technologies for mechanistic interpretation of omics data. In particular, recent advances in multi-omics measurement and analysis require the development of visualization methods that encompass multiple omics data. Visualization in 2.5 dimension (2.5D visualization), which is an isometric view of stacked X-Y planes, is a convenient way to interpret multi-omics/trans-omics data in the context of the conventional layouts of biochemical networks drawn on each of the stacked omics layers. However, 2.5D visualization of trans-omics networks is a state-of-the-art method that primarily relies on time-consuming human efforts involving manual drawing. Here, we present an R Bioconductor package 'transomics2cytoscape' for automated visualization of 2.5D trans-omics networks. We confirmed that transomics2cytoscape could be used for rapid visualization of trans-omics networks presented in published papers within a few minutes. Transomics2cytoscape allows for frequent update/redrawing of trans-omics networks in line with the progress in multi-omics/trans-omics data analysis, thereby enabling network-based interpretation of multi-omics data at each research step. The transomics2cytoscape source code is available at https://github.com/ecell/transomics2cytoscape ., (© 2024. The Author(s).)

Insulin resistance is the primary pathophysiology underlying metabolic syndrome and type 2 diabetes 1,2 . Previous metagenomic studies have described the characteristics of gut microbiota and their roles in metabolizing major nutrients in insulin resistance 3-9 . In particular, carbohydrate metabolism of commensals has been proposed to contribute up to 10% of the host's overall energy extraction 10 , thereby playing a role in the pathogenesis of obesity and prediabetes 3,4,6 . Nevertheless, the underlying mechanism remains unclear. Here we investigate this relationship using a comprehensive multi-omics strategy in humans. We combine unbiased faecal metabolomics with metagenomics, host metabolomics and transcriptomics data to profile the involvement of the microbiome in insulin resistance. These data reveal that faecal carbohydrates, particularly host-accessible monosaccharides, are increased in individuals with insulin resistance and are associated with microbial carbohydrate metabolisms and host inflammatory cytokines. We identify gut bacteria associated with insulin resistance and insulin sensitivity that show a distinct pattern of carbohydrate metabolism, and demonstrate that insulin-sensitivity-associated bacteria ameliorate host phenotypes of insulin resistance in a mouse model. Our study, which provides a comprehensive view of the host-microorganism relationships in insulin resistance, reveals the impact of carbohydrate metabolism by microbiota, suggesting a potential therapeutic target for ameliorating insulin resistance., (© 2023. The Author(s).)

Metabolic regulation in skeletal muscle is essential for blood glucose homeostasis. Obesity causes insulin resistance in skeletal muscle, leading to hyperglycemia and type 2 diabetes. In this study, we performed multiomic analysis of the skeletal muscle of wild-type (WT) and leptin-deficient obese (ob/ob) mice, and constructed regulatory transomic networks for metabolism after oral glucose administration. Our network revealed that metabolic regulation by glucose-responsive metabolites had a major effect on WT mice, especially carbohydrate metabolic pathways. By contrast, in ob/ob mice, much of the metabolic regulation by glucose-responsive metabolites was lost and metabolic regulation by glucose-responsive genes was largely increased, especially in carbohydrate and lipid metabolic pathways. We present some characteristic metabolic regulatory pathways found in central carbon, branched amino acids, and ketone body metabolism. Our transomic analysis will provide insights into how skeletal muscle responds to changes in blood glucose and how it fails to respond in obesity., (© 2022. The Author(s).)

Insulin signaling promotes anabolic metabolism to regulate cell growth through multi-omic interactions. To obtain a comprehensive view of the cellular responses to insulin, we constructed a trans-omic network of insulin action in Drosophila cells that involves the integration of multi-omic data sets. In this network, 14 transcription factors, including Myc, coordinately upregulate the gene expression of anabolic processes such as nucleotide synthesis, transcription, and translation, consistent with decreases in metabolites such as nucleotide triphosphates and proteinogenic amino acids required for transcription and translation. Next, as cell growth is required for cell proliferation and insulin can stimulate proliferation in a context-dependent manner, we integrated the trans-omic network with results from a CRISPR functional screen for cell proliferation. This analysis validates the role of a Myc-mediated subnetwork that coordinates the activation of genes involved in anabolic processes required for cell growth., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

There have been a number of reports about the transcriptional regulatory networks in schizophrenia. However, most of these studies were based on a specific transcription factor or a single dataset, an approach that is inadequate to understand the diverse etiology and underlying common characteristics of schizophrenia. Here we reconstructed and compared the transcriptional regulatory network for lipid metabolism enzymes using 15 public transcriptome datasets of neural cells from schizophrenia patients. Since many of the well-known schizophrenia-related SNPs are in enhancers, we reconstructed a network including enhancer-dependent regulation and found that 53.3 % of the total number of edges (7,577 pairs) involved regulation via enhancers. By examining multiple datasets, we found common and unique transcriptional modes of regulation. Furthermore, enrichment analysis of SNPs that were connected with genes in the transcriptional regulatory networks by eQTL suggested an association with hematological cell counts and some other traits/diseases, whose relationship to schizophrenia was either not or insufficiently reported in previous studies. Based on these results, we suggest that in future studies on schizophrenia, information on genotype, comorbidities and hematological cell counts should be included, along with the transcriptome, for a more detailed genetic stratification and mechanistic exploration of schizophrenia., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Influenza viruses are a major public health problem. Vaccines are the best available countermeasure to induce effective immunity against infection with seasonal influenza viruses; however, the breadth of antibody responses in infection versus vaccination is quite different. Here, we show that nasal infection controls two sequential processes to induce neutralizing IgG antibodies recognizing the hemagglutinin (HA) of heterotypic strains. The first is viral replication in the lung, which facilitates exposure of shared epitopes that are otherwise hidden from the immune system. The second process is the germinal center (GC) response, in particular, IL-4 derived from follicular helper T cells has an essential role in the expansion of rare GC-B cells recognizing the shared epitopes. Therefore, the combination of exposure of the shared epitopes and efficient proliferation of GC-B cells is critical for generating broadly-protective antibodies. These observations provide insight into mechanisms promoting broad protection from virus infection.

Systemic metabolic homeostasis is regulated by inter-organ metabolic cycles involving multiple organs. Obesity impairs inter-organ metabolic cycles, resulting in metabolic diseases. The systemic landscape of dysregulated inter-organ metabolic cycles in obesity has yet to be explored. Here, we measured the transcriptome, proteome, and metabolome in the liver and skeletal muscle and the metabolome in blood of fasted wild-type and leptin-deficient obese ( ob / ob ) mice, identifying components with differential abundance and differential regulation in ob / ob mice. By constructing and evaluating the trans-omic network controlling the differences in metabolic reactions between fasted wild-type and ob / ob mice, we provided potential mechanisms of the obesity-associated dysfunctions of metabolic cycles between liver and skeletal muscle involving glucose-alanine, glucose-lactate, and ketone bodies. Our study revealed obesity-associated systemic pathological mechanisms of dysfunction of inter-organ metabolic cycles., Competing Interests: The authors declare no competing interests., (© 2021 The Authors.)

Impaired glucose tolerance associated with obesity causes postprandial hyperglycemia and can lead to type 2 diabetes. To study the differences in liver metabolism in healthy and obese states, we constructed and analyzed transomics glucose-responsive metabolic networks with layers for metabolites, expression data for metabolic enzyme genes, transcription factors, and insulin signaling proteins from the livers of healthy and obese mice. We integrated multiomics time course data from wild-type and leptin-deficient obese ( ob / ob ) mice after orally administered glucose. In wild-type mice, metabolic reactions were rapidly regulated within 10 min of oral glucose administration by glucose-responsive metabolites, which functioned as allosteric regulators and substrates of metabolic enzymes, and by Akt-induced changes in the expression of glucose-responsive genes encoding metabolic enzymes. In ob / ob mice, the majority of rapid regulation by glucose-responsive metabolites was absent. Instead, glucose administration produced slow changes in the expression of carbohydrate, lipid, and amino acid metabolic enzyme-encoding genes to alter metabolic reactions on a time scale of hours. Few regulatory events occurred in both healthy and obese mice. Thus, our transomics network analysis revealed that regulation of glucose-responsive liver metabolism is mediated through different mechanisms in healthy and obese states. Rapid changes in allosteric regulators and substrates and in gene expression dominate the healthy state, whereas slow changes in gene expression dominate the obese state., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Skeletal muscle adaptation is mediated by cooperative regulation of metabolism, signal transduction, and gene expression. However, the global regulatory mechanism remains unclear. To address this issue, we performed electrical pulse stimulation (EPS) in differentiated C2C12 myotubes at low and high frequency, carried out metabolome and transcriptome analyses, and investigated phosphorylation status of signaling molecules. EPS triggered extensive and specific changes in metabolites, signaling phosphorylation, and gene expression during and after EPS in a frequency-dependent manner. We constructed trans-omic network by integrating these data and found selective activation of the pentose phosphate pathway including metabolites, upstream signaling molecules, and gene expression of metabolic enzymes after high-frequency EPS. We experimentally validated that activation of these molecules after high-frequency EPS was dependent on reactive oxygen species (ROS). Thus, the trans-omic analysis revealed ROS-dependent activation in signal transduction, metabolome, and transcriptome after high-frequency EPS in C2C12 myotubes, shedding light on possible mechanisms of muscle adaptation., Competing Interests: The authors declare no conflicts of interest., (© 2020 The Authors.)

Insulin regulates glucose metabolism through thousands of regulatory mechanisms; however, which regulatory mechanisms are keys to control glucose metabolism remains unknown. Here, we performed kinetic trans-omic analysis by integrating isotope-tracing glucose flux and phosphoproteomic data from insulin-stimulated adipocytes and built a kinetic mathematical model to identify key allosteric regulatory and phosphorylation events for enzymes. We identified nine reactions regulated by allosteric effectors and one by enzyme phosphorylation and determined the regulatory mechanisms for three of these reactions. Insulin stimulated glycolysis by promoting Glut4 activity by enhancing phosphorylation of AS160 at S595, stimulated fatty acid synthesis by promoting Acly activity through allosteric activation by glucose 6-phosphate or fructose 6-phosphate, and stimulated glutamate synthesis by alleviating allosteric inhibition of Gls by glutamate. Most of glycolytic reactions were regulated by amounts of substrates and products. Thus, phosphorylation or allosteric modulator-based regulation of only a few key enzymes was sufficient to change insulin-induced metabolism., Competing Interests: The authors declare no competing interests., (© 2020 The Author(s).)

Cellular metabolism is dynamic, but quantifying non-steady metabolic fluxes by stable isotope tracers presents unique computational challenges. Here, we developed an efficient 13 C-tracer dynamic metabolic flux analysis (13C-DMFA) framework for modeling central carbon fluxes that vary over time. We used B-splines to generalize the flux parameterization system and to improve the stability of the optimization algorithm. As proof of concept, we investigated how 3T3-L1 cultured adipocytes acutely metabolize glucose in response to insulin. Insulin rapidly stimulates glucose uptake, but intracellular pathways responded with differing speeds and magnitudes. Fluxes in lower glycolysis increased faster than those in upper glycolysis. Glycolysis fluxes rose disproportionally larger and faster than the tricarboxylic acid cycle, with lactate a primary glucose end product. The uncovered array of flux dynamics suggests that glucose catabolism is additionally regulated beyond uptake to help shunt glucose into appropriate pathways. This work demonstrates the value of using dynamic intracellular fluxes to understand metabolic function and pathway regulation., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Cellular signaling regulates various cellular functions via protein phosphorylation. Phosphoproteomic data potentially include information for a global regulatory network from signaling to cellular functions, but a procedure to reconstruct this network using such data has yet to be established. In this paper, we provide a procedure to reconstruct a global regulatory network from signaling to cellular functions from phosphoproteomic data by integrating prior knowledge of cellular functions and inference of the kinase-substrate relationships (KSRs). We used phosphoproteomic data from insulin-stimulated Fao hepatoma cells and identified protein phosphorylation regulated by insulin specifically over-represented in cellular functions in the KEGG database. We inferred kinases for protein phosphorylation by KSRs, and connected the kinases in the insulin signaling layer to the phosphorylated proteins in the cellular functions, revealing that the insulin signal is selectively transmitted via the Pi3k-Akt and Erk signaling pathways to cellular adhesions and RNA maturation, respectively. Thus, we provide a method to reconstruct global regulatory network from signaling to cellular functions based on phosphoproteomic data., (© 2018 Molecular Biology Society of Japan and John Wiley & Sons Australia, Ltd.)

The concentrations of insulin selectively regulate multiple cellular functions. To understand how insulin concentrations are interpreted by cells, we constructed a trans-omic network of insulin action in FAO hepatoma cells using transcriptomic data, western blotting analysis of signaling proteins, and metabolomic data. By integrating sensitivity into the trans-omic network, we identified the selective trans-omic networks stimulated by high and low doses of insulin, denoted as induced and basal insulin signals, respectively. The induced insulin signal was selectively transmitted through the pathway involving Erk to an increase in the expression of immediate-early and upregulated genes, whereas the basal insulin signal was selectively transmitted through a pathway involving Akt and an increase of Foxo phosphorylation and a reduction of downregulated gene expression. We validated the selective trans-omic network in vivo by analysis of the insulin-clamped rat liver. This integrated analysis enabled molecular insight into how liver cells interpret physiological insulin signals to regulate cellular functions., (Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.)

Insulin triggers an extensive signaling cascade to coordinate adipocyte glucose metabolism. It is considered that the major role of insulin is to provide anabolic substrates by activating GLUT4-dependent glucose uptake. However, insulin stimulates phosphorylation of many metabolic proteins. To examine the implications of this on glucose metabolism, we performed dynamic tracer metabolomics in cultured adipocytes treated with insulin. Temporal analysis of metabolite concentrations and tracer labeling revealed rapid and distinct changes in glucose metabolism, favoring specific glycolytic branch points and pyruvate anaplerosis. Integrating dynamic metabolomics and phosphoproteomics data revealed that insulin-dependent phosphorylation of anabolic enzymes occurred prior to substrate accumulation. Indeed, glycogen synthesis was activated independently of glucose supply. We refer to this phenomenon as metabolic priming, whereby insulin signaling creates a demand-driven system to "pull" glucose into specific anabolic pathways. This complements the supply-driven regulation of anabolism by substrate accumulation and highlights an additional role for insulin action in adipocyte glucose metabolism., (Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.)

Two recent studies in Cell and Science demonstrate the reconstruction of global mechanistic networks and identification of regulatory principles from multi-omics data., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Secretion of insulin transiently increases after eating, resulting in a high circulating concentration. Fasting limits insulin secretion, resulting in a low concentration of insulin in the circulation. We analyzed transcriptional responses to different temporal patterns and doses of insulin in the hepatoma FAO cells and identified 13 up-regulated and 16 down-regulated insulin-responsive genes (IRGs). The up-regulated IRGs responded more rapidly than did the down-regulated IRGs to transient stepwise or pulsatile increases in insulin concentration, whereas the down-regulated IRGs were repressed at lower concentrations of insulin than those required to stimulate the up-regulated IRGs. Mathematical modeling of the insulin response as two stages-(i) insulin signaling to transcription and (ii)transcription and mRNA stability-indicated that the first stage was the more rapid stage for the down-regulated IRGs, whereas the second stage of transcription was the more rapid stage for the up-regulated IRGs. A subset of the IRGs that were up-regulated or down-regulated in the FAO cells was similarly regulated in the livers of rats injected with a single dose of insulin. Thus, not only can cells respond to insulin but they can also interpret the intensity and pattern of signal to produce distinct transcriptional responses. These results provide insight that may be useful in treating obesity and type 2 diabetes associated with aberrant insulin production or tissue responsiveness., (Copyright © 2016, American Association for the Advancement of Science.)

We propose 'trans-omic' analysis for reconstructing global biochemical networks across multiple omic layers by use of both multi-omic measurements and computational data integration. We introduce technologies for connecting multi-omic data based on prior knowledge of biochemical interactions and characterize a biochemical trans-omic network by concepts of a static and dynamic nature. We introduce case studies of metabolism-centric trans-omic studies to show how to reconstruct a biochemical trans-omic network by connecting multi-omic data and how to analyze it in terms of the static and dynamic nature. We propose a trans-ome-wide association study (trans-OWAS) connecting phenotypes with trans-omic networks that reflect both genetic and environmental factors, which can characterize several complex lifestyle diseases as breakdowns in the trans-omic system., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Cellular homeostasis is regulated by signals through multiple molecular networks that include protein phosphorylation and metabolites. However, where and when the signal flows through a network and regulates homeostasis has not been explored. We have developed a reconstruction method for the signal flow based on time-course phosphoproteome and metabolome data, using multiple databases, and have applied it to acute action of insulin, an important hormone for metabolic homeostasis. An insulin signal flows through a network, through signaling pathways that involve 13 protein kinases, 26 phosphorylated metabolic enzymes, and 35 allosteric effectors, resulting in quantitative changes in 44 metabolites. Analysis of the network reveals that insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, thereby controlling a key reaction in glycolysis. We thus provide a versatile method of reconstruction of signal flow through the network using phosphoproteome and metabolome data., (Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.)

Cells use common signaling molecules for the selective control of downstream gene expression and cell-fate decisions. The relationship between signaling molecules and downstream gene expression and cellular phenotypes is a multiple-input and multiple-output (MIMO) system and is difficult to understand due to its complexity. For example, it has been reported that, in PC12 cells, different types of growth factors activate MAP kinases (MAPKs) including ERK, JNK, and p38, and CREB, for selective protein expression of immediate early genes (IEGs) such as c-FOS, c-JUN, EGR1, JUNB, and FOSB, leading to cell differentiation, proliferation and cell death; however, how multiple-inputs such as MAPKs and CREB regulate multiple-outputs such as expression of the IEGs and cellular phenotypes remains unclear. To address this issue, we employed a statistical method called partial least squares (PLS) regression, which involves a reduction of the dimensionality of the inputs and outputs into latent variables and a linear regression between these latent variables. We measured 1,200 data points for MAPKs and CREB as the inputs and 1,900 data points for IEGs and cellular phenotypes as the outputs, and we constructed the PLS model from these data. The PLS model highlighted the complexity of the MIMO system and growth factor-specific input-output relationships of cell-fate decisions in PC12 cells. Furthermore, to reduce the complexity, we applied a backward elimination method to the PLS regression, in which 60 input variables were reduced to 5 variables, including the phosphorylation of ERK at 10 min, CREB at 5 min and 60 min, AKT at 5 min and JNK at 30 min. The simple PLS model with only 5 input variables demonstrated a predictive ability comparable to that of the full PLS model. The 5 input variables effectively extracted the growth factor-specific simple relationships within the MIMO system in cell-fate decisions in PC12 cells.

Insulin governs systemic glucose metabolism, including glycolysis, gluconeogenesis and glycogenesis, through temporal change and absolute concentration. However, how insulin-signalling pathway selectively regulates glycolysis, gluconeogenesis and glycogenesis remains to be elucidated. To address this issue, we experimentally measured metabolites in glucose metabolism in response to insulin. Step stimulation of insulin induced transient response of glycolysis and glycogenesis, and sustained response of gluconeogenesis and extracellular glucose concentration (GLC(ex)). Based on the experimental results, we constructed a simple computational model that characterises response of insulin-signalling-dependent glucose metabolism. The model revealed that the network motifs of glycolysis and glycogenesis pathways constitute a feedforward (FF) with substrate depletion and incoherent feedforward loop (iFFL), respectively, enabling glycolysis and glycogenesis responsive to temporal changes of insulin rather than its absolute concentration. In contrast, the network motifs of gluconeogenesis pathway constituted a FF inhibition, enabling gluconeogenesis responsive to absolute concentration of insulin regardless of its temporal patterns. GLC(ex) was regulated by gluconeogenesis and glycolysis. These results demonstrate the selective control mechanism of glucose metabolism by temporal patterns of insulin.

A latent process involving signal transduction and gene expression is needed as a preparation step for cellular function. We previously found that nerve growth factor (NGF)-induced cell differentiation has a latent process, which is dependent on ERK activity and gene expression and required for subsequent neurite extension. A latent process can be considered as a preparation step that decodes extracellular stimulus information into cellular functions; however, molecular mechanisms of this process remain unknown. We identified Metrnl, Dclk1 and Serpinb1a as genes that are induced during the latent process (LP) with distinct temporal expression profiles and are required for subsequent neurite extension in PC12 cells. The LP genes showed distinct dependency on the duration of ERK activity, and they were also induced during the latent process of PACAP- and forskolin-induced cell differentiation. Regardless of neurotrophic factors, expression levels of the LP genes during the latent process (0-12 hours), but not phosphorylation levels of ERK, always correlated with subsequent neurite extension length (12-24 hours). Overexpression of all LP genes together, but not of each gene separately, enhanced NGF-induced neurite extension. The LP gene products showed distinct spatial localization. Thus, the LP genes appear to be the common decoders for neurite extension length regardless of neurotrophic factors, and they might function in distinct temporal and spatial manners during the latent process. Our findings provide molecular insight into the physiological meaning of the latent process as the preparation step for decoding information for future phenotypic change.

Background: Appropriate regulation of respective gene expressions is a bottleneck for the realization of artificial biological systems inside living cells. The modification of several promoter sequences is required to achieve appropriate regulation of the systems. However, a time-consuming process is required for the insertion of an operator, a binding site of a protein for gene expression, to the gene regulatory region of a plasmid. Thus, a standardized method for integrating operator sequences to the regulatory region of a plasmid is required., Results: We developed a standardized method for integrating operator sequences to the regulatory region of a plasmid and constructed a synthetic promoter that functions as a genetic AND gate. By standardizing the regulatory region of a plasmid and the operator parts, we established a platform for modular assembly of the operator parts. Moreover, by assembling two different operator parts on the regulatory region, we constructed a regulatory device with an AND gate function., Conclusions: We implemented a new standard to assemble operator parts for construction of functional genetic logic gates. The logic gates at the molecular scale have important implications for reprogramming cellular behavior.

Shoc2/SUR-8 positively regulates Ras/ERK MAP kinase signaling by serving as a scaffold for Ras and Raf. Here, we examined the role of Shoc2 in the spatio-temporal regulation of Ras by using a fluorescence resonance energy transfer (FRET)-based biosensor, together with computational modeling. In epidermal growth factor-stimulated HeLa cells, RNA-mediated Shoc2 knockdown reduced the phosphorylation of MEK and ERK with half-maximal inhibition, but not the activation of Ras. For the live monitoring of Ras binding to Raf, we utilized a FRET biosensor wherein Ras and the Ras-binding domain of Raf were connected tandemly and sandwiched with acceptor and donor fluorescent proteins for the FRET measurement. With this biosensor, we found that Shoc2 was required for the rapid interaction of Ras with Raf upon epidermal growth factor stimulation. To decipher the molecular mechanisms underlying the kinetics, we developed two computational models that might account for the action of Shoc2 in the Ras-ERK signaling. One of these models, the Shoc2 accelerator model, provided a reasonable explanation of the experimental observations. In this Shoc2 accelerator model, Shoc2 accelerated both the association and dissociation of Ras-Raf interaction. We propose that Shoc2 regulates the spatio-temporal patterns of the Ras-ERK signaling pathway primarily by accelerating the Ras-Raf interaction.

Analysis of cellular components at multiple levels of biological information can provide valuable functional insights. We performed multiple high-throughput measurements to study the response of Escherichia coli cells to genetic and environmental perturbations. Analysis of metabolic enzyme gene disruptants revealed unexpectedly small changes in messenger RNA and proteins for most disruptants. Overall, metabolite levels were also stable, reflecting the rerouting of fluxes in the metabolic network. In contrast, E. coli actively regulated enzyme levels to maintain a stable metabolic state in response to changes in growth rate. E. coli thus seems to use complementary strategies that result in a metabolic network robust against perturbations.

Background: Many computer studies have employed either dynamic simulation or metabolic flux analysis (MFA) to predict the behaviour of biochemical pathways. Dynamic simulation determines the time evolution of pathway properties in response to environmental changes, whereas MFA provides only a snapshot of pathway properties within a particular set of environmental conditions. However, owing to the large amount of kinetic data required for dynamic simulation, MFA, which requires less information, has been used to manipulate large-scale pathways to determine metabolic outcomes., Results: Here we describe a simulation method based on cooperation between kinetics-based dynamic models and MFA-based static models. This hybrid method enables quasi-dynamic simulations of large-scale metabolic pathways, while drastically reducing the number of kinetics assays needed for dynamic simulations. The dynamic behaviour of metabolic pathways predicted by our method is almost identical to that determined by dynamic kinetic simulation., Conclusion: The discrepancies between the dynamic and the hybrid models were sufficiently small to prove that an MFA-based static module is capable of performing dynamic simulations as accurately as kinetic models. Our hybrid method reduces the number of biochemical experiments required for dynamic models of large-scale metabolic pathways by replacing suitable enzyme reactions with a static module.

Unlabelled: A computational tool for mitochondrial systems biology has been developed as a simulation model of E-Cell2, a publicly available simulation system. The general model consists of 58 enzymatic reactions and 117 metabolites, representing the respiratory chain, the TCA cycle, the fatty acid beta-oxidation and the inner-membrane transport system. It is based on previously published enzyme kinetics studies in the literature; we have successfully integrated and packaged them into a single large model. The model can be easily extended and modified so that mitochondrial biologists/physiologists can integrate their own models and evaluate them in the context of the whole organelle metabolism., Availability: The mitochondrial model is bundled up with E-Cell2 simulation system, which can be downloaded from http://www.e-cell.org. CD-ROMs are also available and are distributed at major conferences., Supplementary Information: All the kinetic data are available via http://www.e-cell.org

Fragrant hydroxyfuranone and dihydroxypyranone derivatives generated in the Maillard reaction of sugars and amino acids are detected in various processed foods and have been shown active to break DNA single-strand in the in vitro studies. In the present study, absorption of 2,5-dimethyl-4-hydroxy-3(2 H)-furanone (DMHF) and 4-hydroxy-2(or 5)-ethyl-5(or 2)-methyl-3(2 H)-furanone (HEMF), both found in soy sauce, into plasma after a single intraperitoneal or oral administration at doses of 0.5-1.0 gkg-1 to mice was examined. Both compounds appeared in plasma 15 min after intraperitoneal administration and disappeared 2 h after the administration. They appeared in plasma 5 min after oral administration, reached maximum after 15-45 min, and gradually disappeared after 2 h, indicating that they are absorbed by the digestive tract. Both DMHF and HEMF induced micronucleated reticulocytes (MNRETs) in mouse peripheral blood in a dose-dependent manner after oral administration. The results indicate that DMHF and HEMF can cause genetic damage after oral administration.

We present E-CELL, a generic computer software environment for modeling a cell and conducting experiments in silico. The E-CELL system allows a user to define functions of proteins, protein-protein interactions, protein-DNA interactions, regulation of gene expression and other features of cellular metabolism, in terms of a set of reaction rules. The system then executes those reactions iteratively, and the user can observe, through a computer display, dynamic changes in concentrations of proteins, protein complexes and other chemical compounds in the cell. Using this software, we constructed a model of a hypothetical cell with only 127 genes sufficient for transcription, translation, energy production and phospholipid synthesis. Most of the genes are taken from Mycoplasma genitalium, the organism having the smallest known chromosome, whose complete 580kb genome sequence was determined at TIGR in 1995. We discuss future applications of the E-CELL system with special respect to genome engineering.